In wind turbine control systems of the state of the art, alarm masks are used to determine if a wind turbine, or one of its components, is in a critical condition. If, for instance, the amount of vibration that is measured for a certain component of the wind turbine exceeds a certain value or condition as defined by the corresponding alarm mask, a critical operational state is present.
Typically, measurement values are associated with their corresponding wind turbine components by condition variables. In this manner, the measured value of the condition variable representing a certain wind turbine component or subsystem can give an accurate account of the present operational condition of that subsystem.
However, the measured values of the condition variables can vary between turbines of the same type and construction, since the dynamic behaviour of each wind turbine is individual to that particular turbine to a certain degree, although these wind turbines are of the same size and construction.
Thus, applying the same mask to all wind turbines of the same type can lead to an excessive number of false alarms for some of these wind turbines, while, at the same time, an unacceptably low detection probability is provided for other of these wind turbines.
Consequently, for each individual wind turbine, the alarm masks needed for proper condition monitoring have to be configured individually. Since this is usually performed manually in systems of the state of the art, the process of alarm mask configuration is costly and time-consuming.
U.S. Pat. No. 6,785,637 B1 shows a system that involves evaluating differences between currently recorded vibration spectra and previously recorded reference spectra. If the system detects deviations which exceed a predetermined threshold value, a fault message is produced.
P. Caselitz, J. Giebhardt, and R. Kewitsch propose, in their article “Advanced Condition Monitoring System for Wind Energy Converters”, proceedings EWEC '99, Nice, France, pages 63 to 66, a wind turbine condition monitoring system which provides a learning mode and in which alarm levels are generated based on mean values and standard deviations of measurement values during normal and fault-free operation.